UC Riverside

Atmospheric dispersion models play a critical role in the management of air quality. They are used to estimate the impact of sources of air pollutants on air quality. They are also used to infer emissions from sources from measurements of pollutant concentration. Regulatory agencies used air quality models to determine whether existing or proposed industrial facilities comply with regulatory requirements.

In my research, I developed a class of dispersion models to study four problems. I developed two Lagrangian dispersion models to estimate emissions of wildfires using data from PM monitoring networks and the High-Resolution Rapid Refresh (HRRR) meteorological model. By integrating data from ground-based monitors and NASA's Moderate Resolution Imaging Spectroradiometer (MODIS), I created a comprehensive model that enables improved spatial and temporal resolution for assessing PM2.5 concentrations during wildfires. This integrated technology, capable of evaluating concentrations at 1 km spatial resolution with 1 hour temporal resolution, has significant implications for health risk assessment, evacuation planning, and policy development related to wildfire impacts.

I next demonstrated the application of a dispersion model to estimate emissions of methane (CH4) using total column measurements from four field campaigns conducted in the San Joaquin Valley of California during four seasons from March 2019 until January 2020. The atmospheric column dry mixing ratios of CH4 were retrieved from multiple EM27/SUN solar spectrometers deployed upwind and downwind of a cluster of dairy farms. These measurements were complemented with satellite observations of column-averaged CH4 from the S5P/TROPOMI satellite instrument over the same area to extend the analysis to larger scales and periods.

The next study involved the development and application of a model to estimate the impact of vehicle tailpipe emissions on people waiting next to idling vehicles. We conducted a field study designed to collect CO2 concentration data at distances of a few meters from the tailpipe of a vehicle: the accelerator pedal was controlled to simulate idling and acceleration from a stop. Analysis of the data shows that the measurements are described within a factor of two with a dispersion model that uses micrometeorological variables as inputs and includes plume rise associated with the buoyancy of the exhaust plume. The data suggest that people situated a few meters from an idling vehicle are likely to be exposed to levels of NO2 that are above the Clean Air Act 1-hour standard of 100 ppb.

The final study examined the application of a dispersion model to estimate PM10 emissions from roads. This involved sampling silt loadings on roads using a mobile dust collection system that I helped to design and build. The sampling was conducted on two freeways and two city roads in Riverside, California. PurpleAir PM monitors and PICARRO CO2 monitors were deployed on the mobile platform to measure road dust concentrations, which was then used to infer emission factors with a line source dispersion model, and carbon mass balance approach. The results indicated that freeways have lower emission factors compared to the city roads. The data from the field studies was used to propose a new model for emission factors, which improves upon the currently used regulatory model.